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Update app.py
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app.py
CHANGED
@@ -3,14 +3,19 @@ import torch
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import numpy as np
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from transformers import DPTForDepthEstimation, DPTImageProcessor
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import gradio as gr
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256", torch_dtype=torch.
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processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
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color_map = cv2.applyColorMap(np.arange(256, dtype=np.uint8), cv2.COLORMAP_INFERNO)
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input_tensor = torch.zeros((1, 3, 128, 128), dtype=torch.
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depth_map = np.zeros((128, 128), dtype=np.float32)
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depth_map_colored = np.zeros((128, 128, 3), dtype=np.uint8)
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import numpy as np
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from transformers import DPTForDepthEstimation, DPTImageProcessor
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import gradio as gr
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import torch.quantization
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256", torch_dtype=torch.float32)
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model.eval()
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model = torch.quantization.quantize_dynamic(
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model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8
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).to(device)
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processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
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color_map = cv2.applyColorMap(np.arange(256, dtype=np.uint8), cv2.COLORMAP_INFERNO)
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input_tensor = torch.zeros((1, 3, 128, 128), dtype=torch.float32, device=device)
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depth_map = np.zeros((128, 128), dtype=np.float32)
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depth_map_colored = np.zeros((128, 128, 3), dtype=np.uint8)
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